Investigating the Impact of a Mindfulness Intervention on Rumination Patterns via a Source Imaging Approach
Bibliographic record
Abstract
To determine the structures involved in rumination a source-imaging approach was adopted using surface EEG signals. For the purpose of this paper, 17 participants were included: 9 from the low-ruminating group and 8 from the high-ruminating group. Data from the remaining 63 participants will be collected and analyzed for future publication. Participants performed rumination questionnaires, followed by in-lab EEG sessions where their brain activity was measured during resting state for 5 minutes. During the resting-state data collection, the participants were asked to close their eyes and relax; this was to minimize the effects of blink artifacts and lateral eye movements within the data.Using a Linearly Constrained Minimal Variance (LCMV) beamformer, the participant’s EEG data was analyzed within spatial coordinates to determine regions of increased neural activation while at a resting state. The findings determine that there were visual differences between the low-ruminating and the high-ruminating group, most notably the increased activation of the ventromedial prefrontal cortex (vmPFC) in the low-ruminating group and the increased activation of limbic structures in the high-ruminating group. Although differences are shown through visual inspection, the validity of the study can be improved with the inclusion of statistical analyses comparing the high activation regions between both the low-ruminating and high-ruminating group. This study was able to provide evidence that beamforming can be used to determine the structures involved in rumination and opens avenues for future research within this field including determining whether a statistically significant difference in rumination patterns can be observed after a mindfulness intervention. This will be investigated in a future publication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".